Attention-Based AFDM Channel Estimation Network Using Diagonal Reconstruction

Affine Frequency Division Multiplexing (AFDM) has been proposed for future high-mobility communication scenarios. However, existing AFDM channel estimation methods suffer significant performance degradation under fractional Doppler conditions due to path energy dispersion. To address this issue, we propose a deep learning network that adaptively learns path energy dispersion through a 1D processing module and a Transformer block, based on the diagonal reconstruction of the AFDM effective channel matrix. 1D processing module employs convolutions with different kernel sizes to extract pilot features, and Transformer block models vary energy dispersion patterns. The proposed method does not require prior knowledge of the number of paths and the assumption of distinct path delays. Simulation results demonstrate that at a Signal-to-Noise Ratio (SNR) of 25 dB, the proposed method achieves up to a 4 dB gain in Normalized Mean Square Error (NMSE) and an 6 dB improvement in Bit Error Rate (BER) over existing traditional methods under fractional Doppler conditions.